Luke, for the sake of correctness, Debug.Writeline call was added to the MulTask() method, which was executed after the sequential and PFor multiplication. So, PFor loop ran without any blocking on screen output, and it was slower than sequential presumably
because of all the extra work associated with priming up parallel execution environment.

If 90% of the input for my app on any given day happens to be small, it's better to process those 90% sequentially and use parallel execution only when appropriate, but for that it would be nice to know, where (approximately) is that cutting point.

I can run tests and collect some stats on what overhead of firing up parallel execution is, but assuming that this work might have been already done while developing the parallel framework, it would be preferrable for me to look at the stats collected by
the PF development team than spend time and efforts myself.

One thing though, when matrix multiplication was executed on a small data set, sequential was actually faster than ParallelFor (@20:48). Are there any insights on estimating an overhead of setting up parallel execution machinery, so, application could attempt
guessing whether to process data set sequentially or in-parallel (assuming that application knows the size of the data set)?

Thanks Mike. It sounds like registering contracts with the framework would be the core enabling technology for proper blame assignment and proactive failure prevention. It almost can be read as you guys are planning start working on that

I wonder if it is or someday would be possible to interrogate a method about its contracts at runtime, so the caller could ensure compliance before actually invoking the method? E.g. before sending big batch of data over the wire for pre-processing and loading
into a database in one transaction, I get an abstract code tree from the transformation service that represents all or at least some of the checks and run them locally and perform corrective actions proactively.

Measurements in F# is an exciting feature. No doubt about it. I was really impressed when I learned about it being added to the language.

It surely addresses a lot of potential issues with measurement mismatch.

Yet, run time support for measurements still makes a lot of sense.

If you are reading data from external sources at run-time (files, sensors, or web services), you'd still have to implemet all the measurement tracking and conversion yourself. If this can be married to contract somehow, then application would just have to
tell the framework that it expects mass to be in kilos, and if input feed turns out to be pounds then conversion would happen under the scene.

Another presumably useful feature would be to declare measures off of classes/types. If for example, I'm counting my chicken, I don't want to be able to inadvertently add this to the count of eggs, unless I explicitly coersed chicken and eggs to be "things".

Now, this in-memory columnar database, is it going to be shipped along with Gemini only, or it eventually make it into some edition of SQL Server engine?

Sure, database query language doesn't have to be SQL (or arguably even shouldn't be SQL), but querying language aside, such a compelling feature as in-memory columnar storage looks very appealing as a generic service.

A question though, when a dozen of users load their BI-mini apps onto the server and try to run them all, will there be 10 copies of in-memory database on the server (which might quite easily kill the server on the spot)? Or the server version of columnar
database in not in-memory?